Energy Consumption Forecasting Of Ningxia Based On Time Series Analysis

The economic development of Ningxia mainly relies on fossil fuels. The highest consumption is coal in energy consumption. It is accounted for about half of the gross value of industrial output. The main problems of the energy development in Ningxia included:the unreasonable structure of energy products, energy efficiency is low and the problem such as uneven distribution of energy. In order to ensure to complete the "twelfth five-year" energy conservation and emissions reduction targets stipulated by the state, the district government determined to unit GDP energy consumption in 2015 than in 2010 dropped 15%. Energy-saving situation is quite serious. That problem is a major challenge to the ningxia economic. Therefore, predicting the future consumption of energy has become very important in Ningxia, only accurately predict energy demand can be put forward countermeasures, and then achieve energy conservation and emissions reduction.At first, this paper analyzes the present situation of energy in Ningxia, introduces the time series analysis methods and its related theory, and summarizes the process of ARIMA model established. Then, for the actual data, using respectively the trend extrapolation model, improved trend model, based on three quadratic polynomial of the exponential smoothing model and ARIMA model go to the fitting. The results show that ARIMA model to predict the best effect. Therefore, the ARIMA model is used to predict energy consumption data from 2014 to 2018. Finally, this paper introduces the development of renewable energy in Ningxia and using ARIMA model respectively to predict consumption of fossil energy and renewable energy. Furthermore, the article analysis all kinds of energy consumption change and sums up the problems of energy development. In the end, to present the adjustment of energy structure in Ningxia suggestions, and then help government to make economic development strategy and provides the basis for the government to make the final decision.